A Review Study of the Deep Learning Techniques used for the Classification of Chest Radiological Images for COVID-19 Diagnosis

Wang, Yu; Hargreaves, Carol Anne

doi:10.1016/j.jjimei.2022.100100

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…These results validate an upward trend in attention to DL methods, as also described in the previous section. A lot of recent literature, especially in the medical field, has attempted to address the biggest challenges, mainly derived from data scarcity and model performance [14,[61][62][63][64]. Some research has focused on improving perforce or reducing the computational requirements in models such as CNNs [60,65,66] using techniques such as model pruning or compression.…”

Section: Image Processing Developmentsmentioning

confidence: 99%

“…There is also an ample amount of research using ML algorithms in the medical field. DL techniques have been applied in infection monitoring [64,84,85], in developing personalized advice for treatment [19,86], in diagnosing several diseases like COVID-19 [63,[87][88][89], or imaging procedures including radiology [14,63,90,91] and pathology imaging [19] or in cancer screening [91][92][93][94].…”

Section: Domainsmentioning

confidence: 99%

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Developments in Image Processing Using Deep Learning and Reinforcement Learning

Valente,

António,

Mora

et al. 2023

J. Imaging

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The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for society in general. In addition to being larger, datasets are increasingly complex, bringing new theoretical and computational challenges. Alongside this evolution, data science tools have exploded in popularity over the past two decades due to their myriad of applications when dealing with complex data, their high accuracy, flexible customization, and excellent adaptability. When it comes to images, data analysis presents additional challenges because as the quality of an image increases, which is desirable, so does the volume of data to be processed. Although classic machine learning (ML) techniques are still widely used in different research fields and industries, there has been great interest from the scientific community in the development of new artificial intelligence (AI) techniques. The resurgence of neural networks has boosted remarkable advances in areas such as the understanding and processing of images. In this study, we conducted a comprehensive survey regarding advances in AI design and the optimization solutions proposed to deal with image processing challenges. Despite the good results that have been achieved, there are still many challenges to face in this field of study. In this work, we discuss the main and more recent improvements, applications, and developments when targeting image processing applications, and we propose future research directions in this field of constant and fast evolution.

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Section: Image Processing Developmentsmentioning

confidence: 99%

Section: Domainsmentioning

confidence: 99%

Developments in Image Processing Using Deep Learning and Reinforcement Learning

Valente,

António,

Mora

et al. 2023

J. Imaging

View full text Add to dashboard Cite

show abstract

“…As a result, the accuracy and speed of pneumonia diagnosis could be enhanced by employing intelligent systems, thereby supporting the battle against pneumonia. Recent years have witnessed growing interest in machine learning systems for pneumonia diagnosis [5] (New Reference), although further research is required to verify their effectiveness and reliability [3].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Pneumonia Diagnosis Using Chest X-Ray Image Features and Multilayer Perceptron and k-NN Machine Learning Algorithms

Çelik¹,

Demi̇rel²

2023

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Pneumonia poses a significant risk of mortality, particularly in individuals with compromised immune systems, necessitating early diagnosis and treatment to combat the disease effectively. In this study, we employed Multilayer Perceptron (MLP) and k-Nearest Neighbors (k-NN) machine learning (ML) algorithms to facilitate pneumonia diagnosis using preprocessed Chest X-ray images. Preprocessing steps, including Histogram Equalization, Mask R-CNN (Mask Region-Based Convolutional Neural Network), and Otsu thresholding, were successively performed on the images. Textural features were subsequently extracted from the Chest X-ray images and utilized as inputs for the classification algorithms. To address the imbalanced class problem in the training data, the Synthetic Minority Over-sampling Technique (SMOTE) was implemented. Classification evaluation metrics included accuracy, precision, recall, F1-Score, and AUC Score (Area Under Curve Score). The results revealed that the MLP algorithm outperformed the k-NN algorithm across all metrics. Furthermore, a comparison of the MLP and k-NN algorithms with previous studies in the literature demonstrated the superiority of the MLP algorithm, achieving an accuracy of 95.673%, F1-Score of 95.706%, and AUC Score of 99.006%. This study highlights the potential of employing the MLP algorithm for highly accurate pneumonia diagnosis using Chest X-ray images.

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Customized convolutional neural network for pulmonary multi-disease classification using chest x-ray images

Bhosale

Yadav²

2023

Multimed Tools Appl

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A Review Study of the Deep Learning Techniques used for the Classification of Chest Radiological Images for COVID-19 Diagnosis

Cited by 8 publications

References 39 publications

Developments in Image Processing Using Deep Learning and Reinforcement Learning

Developments in Image Processing Using Deep Learning and Reinforcement Learning

Enhanced Pneumonia Diagnosis Using Chest X-Ray Image Features and Multilayer Perceptron and k-NN Machine Learning Algorithms

Customized convolutional neural network for pulmonary multi-disease classification using chest x-ray images

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